{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import selenium\n",
    "import pandas  # 当前爬取部分pandas用于承接数据以获取更好的格式\n",
    "import time\n",
    "import datetime\n",
    "import numpy\n",
    "import random\n",
    "import traceback\n",
    "import threading\n",
    "import queue\n",
    "\n",
    "\n",
    "# 车型关系--待完善，后续待交路表部分完成后会使用对应映射而不是上面的简单类型映射\n",
    "marshalling = {'CHR380BL': [], 'CHR380B': [], 'CHR380B重连': []}\n",
    "\n",
    "\n",
    "species = {'新空调普快': ['120', 'LPPPPPP', 0], '新空调快速': ['120', 'LPPPPPP', 0],\n",
    "           '新空调特快': ['140', 'LPPPPPP', 0], '新空调直快': ['160', 'LPPPPPP', 0],\n",
    "           '动车': ['200', 'LPPL', 1], '城际': ['200', 'LPPL', 1], '高速': ['300', 'LPPLLPPL', 2]}\n",
    "species1 = {'K': ['120', 'LPPPPPP', 0], 'T': ['140', 'LPPPPPP', 0], 'Z': ['160', 'LPPPPPP', 0],\n",
    "            'D': ['200', 'LPPL', 1], 'C': ['200', 'LPPL', 1], 'G': ['300', 'LPPLLPPL', 2]}\n",
    "\n",
    "station = {'武汉': ['a', -1, 0, 0, 0], '武汉动车所': ['c', 1, 0, 0, 20],\n",
    "           '京广高速咸宁北方向': ['b', 1, 2, 1, 5], '武九客专葛店南方向': ['d', 1, 2, 1, 4],\n",
    "           '京广高速孝感北方向': ['e', 1, 1, 2, 10], '沪蓉线红安西方向': ['f', 0, 1, 2, 10],\n",
    "           '沪蓉线汉口方向': ['h', 0, 1, 2, 5], '丹水池联络线汉口方向': ['i', 0, 1, 2, 7], }\n",
    "\n",
    "# 线路和车站关系，主要用于从车站-值获取线路-键\n",
    "routewhchar = {'武汉动车所': [\"武汉\"],\n",
    "               '京广高速咸宁北方向': [\"咸宁北\", \"岳阳东\", \"长沙南\"], '武九客专葛店南方向': [\"葛店南\", \"鄂州\", \"黄冈西\", \"黄冈东\", \"黄梅东\", \"黄石北\", \"庐山\"],\n",
    "               '京广高速孝感北方向': [\"孝感北\", \"信阳东\", \"漯河西\", \"驻马店东\", \"郑州东\"], '沪蓉线红安西方向': [\"红安西\", \"麻城北\", \"六安\", \"合肥\", \"合肥南\"],\n",
    "               '沪蓉线汉口方向': [\"汉口\"], '丹水池联络线汉口方向': [\"汉口\"], }\n",
    "routewh1 = {'c': [\"武汉\"],\n",
    "            'b': [\"咸宁北\", \"岳阳东\", \"长沙南\"], 'd': [\"葛店南\", \"鄂州\", \"黄冈西\", \"黄冈东\", \"黄梅东\", \"黄石北\", \"庐山\"],\n",
    "            'e': [\"孝感北\", \"信阳东\", \"漯河西\", \"驻马店东\", \"郑州东\"], 'f': [\"红安西\", \"麻城北\", \"六安\", \"合肥\", \"合肥南\"],\n",
    "            'f': [\"汉口\"], 'i': [\"汉口\"], }\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def dealTime(datafr: pandas.DataFrame) -> pandas.DataFrame:\n",
    "    # 对于始发车和终到车进行时间处理，默认相同\n",
    "    for idx, row in datafr.iterrows():\n",
    "        if row[\"开时\"] == \"----\":\n",
    "            row[\"开时\"] = row[\"到时\"]\n",
    "        elif row[\"到时\"] == \"----\":\n",
    "            row[\"到时\"] = row[\"开时\"]\n",
    "    # datafr['到时'].replace(to_replace=\"----\", method=\"bfill\", inplace=True)\n",
    "    # datafr['开时'].replace(to_replace=\"----\", method=\"ffill\", inplace=True)\n",
    "\n",
    "    # print(datafr)\n",
    "    # 计算停站时间\n",
    "    datafr[\"到时\"] = pandas.to_datetime(datafr[\"到时\"], errors=\"ignore\")\n",
    "    datafr[\"开时\"] = pandas.to_datetime(datafr[\"开时\"], errors=\"ignore\")\n",
    "    # 实发终到设置停站时间\n",
    "    # pd[\"停站时间\"].replace(0,10,inplace=True)\n",
    "\n",
    "    return datafr\n",
    "\n",
    "\n",
    "def stopStTime(stopTime: pandas.Timedelta) -> int:\n",
    "    a = int(stopTime.seconds/60)  # 处理为数值类型的分钟时间\n",
    "    # 始发或是终到的时间是随机的\n",
    "    return a if a > 0 else random.randint(10, 30)\n",
    "\n",
    "\n",
    "def ModtimeStr(switchTime: pandas.Timestamp) -> str:\n",
    "    # 处理为仅有时分秒样式的字符串格式的时间\n",
    "    return switchTime.strftime(\"%H:%M:%S\")\n",
    "\n",
    "\n",
    "def speMarType(traincode: str) -> str:  # 目前获取编组信息的方式\n",
    "    til = species1[traincode[0]]  # 处理获取车辆信息字符串部分\n",
    "    return \"{cod} COMMUTER {speed} {mar} X1\".format(cod=traincode, speed=til[0], mar=til[1])\n",
    "\n",
    "\n",
    "def arrLeaTime(t1: pandas.Timestamp, st: list[str], mark: int) -> str:\n",
    "    # 选取是上一站还是下一站\n",
    "    tarst = st[mark]\n",
    "    useTime = station[tarst][4]\n",
    "    if mark == 0:  # 进场减时间\n",
    "        res = t1-datetime.timedelta(minutes=useTime)\n",
    "    else:  # 离场加时间\n",
    "        res = t1+datetime.timedelta(minutes=useTime)\n",
    "    # 返回时分秒格式的字符串\n",
    "    return res.strftime(\"%H:%M:%S\")\n",
    "\n",
    "# 手动建立映射关系,别的方法不会\n",
    "\n",
    "\n",
    "def getArrLeaSt(stype, station):\n",
    "    stcode = \"\"\n",
    "    # 多路径车站部分\n",
    "    if stype == \"高速\":  # 高速\n",
    "        ArrLeaveSt = {\n",
    "            '汉口': '沪蓉线汉口方向',\n",
    "            '宜昌东': '沪蓉线汉口方向',\n",
    "            '襄阳东': '沪蓉线汉口方向',\n",
    "            '成都东': '沪蓉线汉口方向',\n",
    "            '利川': '沪蓉线汉口方向',\n",
    "            '重庆北': '沪蓉线汉口方向',\n",
    "        }\n",
    "    else:  # 动车城际speedType == \"动车\" or speedType == \"城际\"\n",
    "        ArrLeaveSt = {\n",
    "            '汉口': '丹水池联络线汉口方向',\n",
    "            '宜昌东': '丹水池联络线汉口方向',\n",
    "            '襄阳东': '丹水池联络线汉口方向',\n",
    "            '成都东': '丹水池联络线汉口方向',\n",
    "            '利川': '丹水池联络线汉口方向',\n",
    "            '重庆北': '丹水池联络线汉口方向',\n",
    "        }\n",
    "\n",
    "    stcode = ArrLeaveSt.get(station)\n",
    "    if stcode != None:\n",
    "        return stcode\n",
    "\n",
    "    # 常规单路径车站\n",
    "    ArrLeaveSt = {\n",
    "        '武汉': '武汉动车所',\n",
    "        '南宁东': '京广高速咸宁北方向',\n",
    "        '北京西': '京广高速孝感北方向',\n",
    "        '南昌西': '武九客专葛店南方向',\n",
    "        '香港西九龙': '京广高速咸宁北方向',\n",
    "        '广州南': '京广高速咸宁北方向',\n",
    "\n",
    "        '深圳北': '武九客专葛店南方向',\n",
    "        '西安北': '京广高速孝感北方向',\n",
    "        '温州南': '武九客专葛店南方向',\n",
    "        '兰州西': '京广高速孝感北方向',\n",
    "        '长沙南': '京广高速咸宁北方向',\n",
    "        '郑州东': '京广高速孝感北方向',\n",
    "        '贵阳北': '京广高速咸宁北方向',\n",
    "        '昆明南': '京广高速咸宁北方向',\n",
    "        '上海虹桥': '沪蓉线红安西方向',\n",
    "        '合肥东': '沪蓉线红安西方向',\n",
    "        '厦门': '武九客专葛店南方向',\n",
    "        '南京南': '沪蓉线红安西方向',\n",
    "        '福州': '武九客专葛店南方向',\n",
    "        '杭州东': '武九客专葛店南方向',\n",
    "        '南昌': '武九客专葛店南方向',\n",
    "        '济南': '京广高速孝感北方向',\n",
    "        '青岛北': '京广高速孝感北方向',\n",
    "        '沈阳北': '京广高速孝感北方向',\n",
    "        '厦门北': '武九客专葛店南方向',\n",
    "        '大冶北': '武九客专葛店南方向',\n",
    "        '福州南': '武九客专葛店南方向',\n",
    "        '黄冈东': '武九客专葛店南方向',\n",
    "        '安庆': '武九客专葛店南方向',\n",
    "    }\n",
    "    stcode = ArrLeaveSt.get(station, \"\")\n",
    "    if stcode == \"\":\n",
    "        print(f\"车站:{station} 未找到\")\n",
    "    return stcode\n",
    "\n",
    "\n",
    "def checkin(entrance: str) -> str:\n",
    "    # 终到车等不指定\n",
    "    # print(entrance,end=\" \")\n",
    "    if entrance == '':\n",
    "        return '0'\n",
    "    # 检票口A5 非终到车\n",
    "    try:  # 对于空缺导致的报错\n",
    "        en = entrance.strip(\"检票口AB\")\n",
    "    except TypeError:\n",
    "        return '0'\n",
    "    else:\n",
    "        return en\n",
    "\n",
    "\n",
    "def routedispatch(x, mark=0) -> str:\n",
    "    # 对于分场式车站按线路所分流进场车辆,目前仅设置进场线路所路由\n",
    "    #print(stoptruck,\"\\t\" ,routearr[mark])\n",
    "    t = (x[\"到时\"]-datetime.timedelta(minutes=2)).strftime(\"%H:%M:%S\")\n",
    "    stoptruck = x[\"检票口\"]\n",
    "    if stoptruck >= 1 and stoptruck <= 14:  # 高速场车辆\n",
    "        k1 = {\"武九客专葛店南方向\": f\" p#3#{t}#0\",  # 武汉南线路所\n",
    "              \"沪蓉线汉口方向\": f\" n#2#{t}#0\",  # 滠口线路所\n",
    "              \"丹水池联络线汉口方向\": f\" n#2#{t}#0\"}\n",
    "    else:  # 综合场车辆\n",
    "        k1 = {\n",
    "            \"武九客专葛店南方向\": f\" p#1#{t}#0\",\n",
    "            \"沪蓉线汉口方向\": f\" n#1#{t}#0\",\n",
    "            \"丹水池联络线汉口方向\": f\" n#1#{t}#0\"}\n",
    "\n",
    "    return k1.get(x[\"前后站\"][mark], \"\")\n",
    "\n",
    "\n",
    "def tostrformat(row1):\n",
    "    res = \"\"\n",
    "    try:\n",
    "        indexst = station.get(row1[\"车站名称\"])\n",
    "        res = \" {0}#{1}#{2}#{3}\".format(\n",
    "            indexst[0], row1[\"股道\"], row1[\"到达时间\"], row1[\"停站时间\"])\n",
    "    except KeyError:\n",
    "        print(\"KeyError in:\", str(row1))\n",
    "    finally:\n",
    "        return res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 手动建立映射关系,别的方法不会\n",
    "\n",
    "\n",
    "def processTrains(routefile: str) -> pandas.DataFrame:\n",
    "    # 处理产生初步所需信息的数据框，车次时刻表部分\n",
    "    trainRouteFrame = pandas.read_csv(filepath_or_buffer=routefile,\n",
    "                                      sep=\",\", encoding=\"utf8\", header=0)\n",
    "    # print(trainRouteFrame.head())\n",
    "    trainRouteFrame.drop_duplicates(inplace=True)  # 常规处理\n",
    "    trainRouteFrame.fillna(value=\"\", inplace=True)\n",
    "    trainRouteFrame = dealTime(trainRouteFrame)  # 初步处理时间\n",
    "    trainRouteFrame.reset_index(drop=True, inplace=True)  # 重设索引\n",
    "\n",
    "    trainRouteFrame[\"检票口\"] = trainRouteFrame[\"检票口\"].apply(lambda x: checkin(x))\n",
    "    # 处理检票口和映射进场离场路线\n",
    "    trainRouteFrame[\"arr\"] = trainRouteFrame.apply(\n",
    "        lambda x: getArrLeaSt(x[\"类型\"], x[\"始发站\"]), axis=1)\n",
    "    trainRouteFrame[\"lea\"] = trainRouteFrame.apply(\n",
    "        lambda x: getArrLeaSt(x[\"类型\"], x[\"终到站\"]), axis=1)\n",
    "    trainRouteFrame[\"前后站\"] = trainRouteFrame.apply(\n",
    "        lambda x: [x[\"arr\"], x[\"lea\"]], axis=1)  # 合并前后站\n",
    "\n",
    "    return trainRouteFrame\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "mapdict = {'武汉': 0, '武汉动车所': 1,\n",
    "           '京广高速咸宁北方向': 2, '武九客专葛店南方向': 3,\n",
    "           '京广高速孝感北方向': 4, '沪蓉线红安西方向': 5,\n",
    "           '沪蓉线汉口方向': 6, '丹水池联络线汉口方向': 7, }\n",
    "\n",
    "\n",
    "def processNoEntry(datafr: pandas.DataFrame):\n",
    "    # le = LabelEncoder()  # 数值化处理映射字符类型为数值\n",
    "    # for col in [\"类型\", \"arr\", \"lea\"]:\n",
    "    #     datafr[col] = le.fit_transform(datafr[col])\n",
    "    datafr[\"类型\"] = datafr[\"类型\"].map({\"动车\": 0, \"高速\": 1})\n",
    "\n",
    "    datafr[\"arr\"] = datafr[\"arr\"].map(mapdict)\n",
    "    datafr[\"lea\"] = datafr[\"lea\"].map(mapdict)\n",
    "\n",
    "    # 数值化处理时间类型，转化为数值时间戳,归一化无所谓\n",
    "    datafr[\"开时\"] = datafr[\"开时\"].astype('int64')\n",
    "    datafr[\"到时\"] = datafr[\"到时\"].astype('int64')\n",
    "\n",
    "    datafr[\"检票口\"] = datafr[\"检票口\"].map(float)\n",
    "    # print(datafr.corr())\n",
    "    # 取需要的特征值\n",
    "    dataNumic = datafr.drop(columns=[\"车次\",  \"始发站\", \"终到站\", \"前后站\"])\n",
    "    # 训练用的样本，各项数值均正常\n",
    "    X_d = dataNumic[dataNumic[\"检票口\"] != 0].drop(columns=[\"检票口\"])\n",
    "    y = dataNumic[dataNumic[\"检票口\"] != 0][\"检票口\"]\n",
    "    # 最终需要预测的空值，返回预测的结果\n",
    "    X_test = dataNumic[dataNumic[\"检票口\"] == 0].drop(columns=[\"检票口\"])\n",
    "    # 决策树部分，对于分支和叶节点均给出相对较大的数字\n",
    "    # dts = DecisionTreeClassifier(\n",
    "    #     max_depth=10, min_samples_leaf=3, min_samples_split=6)\n",
    "    dts = RandomForestClassifier(n_estimators=8, random_state=0,\n",
    "                                 max_depth=10, min_samples_leaf=3, min_samples_split=6)\n",
    "\n",
    "    dts.fit(X_d, y)\n",
    "    pre = dts.predict(X_test)  # 预测结果\n",
    "    print(\"训练集上准确率: \", dts.score(X_d, y))  # 正确率\n",
    "    # fig = plt.figure(figsize=(100, 50))  # 可视化决策树图?\n",
    "    # plot_tree(decision_tree=dts, feature_names=[\n",
    "    #     \"arrivetime\", \"leavetime\", \"arrst\", \"least\", \"ckeckin\"], max_depth=5, fontsize=10, filled=True)\n",
    "    # fig.savefig(fname=\"f1.png\")\n",
    "\n",
    "    val, con = numpy.unique(ar=pre, return_counts=True)\n",
    "    print(dict(zip(val, con)))\n",
    "    return pre\n",
    "\n",
    "#at = processTrains(\"wh1.csv\")\n",
    "# processNoEntry(at)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\z'z'x\\AppData\\Local\\Temp\\ipykernel_27032\\2484381920.py:13: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  datafr[\"到时\"] = pandas.to_datetime(datafr[\"到时\"], errors=\"ignore\")\n",
      "C:\\Users\\z'z'x\\AppData\\Local\\Temp\\ipykernel_27032\\2484381920.py:14: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  datafr[\"开时\"] = pandas.to_datetime(datafr[\"开时\"], errors=\"ignore\")\n"
     ]
    },
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       "                    \"layoutIteration\": 32,\n",
       "                    \"orient\": \"horizontal\",\n",
       "                    \"draggable\": true,\n",
       "                    \"emphasis\": {\n",
       "                        \"focus\": \"none\"\n",
       "                    },\n",
       "                    \"label\": {\n",
       "                        \"show\": true,\n",
       "                        \"position\": \"bottom\",\n",
       "                        \"margin\": 8,\n",
       "                        \"fontSize\": 10\n",
       "                    },\n",
       "                    \"lineStyle\": {\n",
       "                        \"show\": true,\n",
       "                        \"width\": 1,\n",
       "                        \"opacity\": 2,\n",
       "                        \"curveness\": 0.5,\n",
       "                        \"type\": \"solid\"\n",
       "                    }\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"show\": true,\n",
       "                    \"text\": \"Sankey-\\u57fa\\u672c\\u793a\\u4f8b\",\n",
       "                    \"target\": \"blank\",\n",
       "                    \"subtarget\": \"blank\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10,\n",
       "                    \"textAlign\": \"auto\",\n",
       "                    \"textVerticalAlign\": \"auto\",\n",
       "                    \"triggerEvent\": false\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"showContent\": true,\n",
       "                \"alwaysShowContent\": false,\n",
       "                \"showDelay\": 0,\n",
       "                \"hideDelay\": 100,\n",
       "                \"enterable\": false,\n",
       "                \"confine\": false,\n",
       "                \"appendToBody\": false,\n",
       "                \"transitionDuration\": 0.4,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5,\n",
       "                \"order\": \"seriesAsc\"\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#5470c6\",\n",
       "                \"#91cc75\",\n",
       "                \"#fac858\",\n",
       "                \"#ee6666\",\n",
       "                \"#73c0de\",\n",
       "                \"#3ba272\",\n",
       "                \"#fc8452\",\n",
       "                \"#9a60b4\",\n",
       "                \"#ea7ccc\"\n",
       "            ]\n",
       "        },\n",
       "        {\n",
       "            \"series\": [\n",
       "                {\n",
       "                    \"type\": \"sankey\",\n",
       "                    \"name\": \"sankey\",\n",
       "                    \"data\": [\n",
       "                        {\n",
       "                            \"name\": \"\\u6b66\\u6c49\\u52a8\\u8f66\\u6240\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u54b8\\u5b81\\u5317\\u65b9\\u5411\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6b66\\u4e5d\\u5ba2\\u4e13\\u845b\\u5e97\\u5357\\u65b9\\u5411\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u5b5d\\u611f\\u5317\\u65b9\\u5411\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6caa\\u84c9\\u7ebf\\u7ea2\\u5b89\\u897f\\u65b9\\u5411\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6caa\\u84c9\\u7ebf\\u6c49\\u53e3\\u65b9\\u5411\\u8fdb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6b66\\u6c49\\u7ad9\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6b66\\u6c49\\u52a8\\u8f66\\u6240\\u79bb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u54b8\\u5b81\\u5317\\u65b9\\u5411\\u79bb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6b66\\u4e5d\\u5ba2\\u4e13\\u845b\\u5e97\\u5357\\u65b9\\u5411\\u79bb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u5b5d\\u611f\\u5317\\u65b9\\u5411\\u79bb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6caa\\u84c9\\u7ebf\\u7ea2\\u5b89\\u897f\\u65b9\\u5411\\u79bb\\u573a\"\n",
       "                        },\n",
       "                        {\n",
       "                            \"name\": \"\\u6caa\\u84c9\\u7ebf\\u6c49\\u53e3\\u65b9\\u5411\\u79bb\\u573a\"\n",
       "                        }\n",
       "                    ],\n",
       "                    \"links\": [\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u52a8\\u8f66\\u6240\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u54b8\\u5b81\\u5317\\u65b9\\u5411\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 1\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u4e5d\\u5ba2\\u4e13\\u845b\\u5e97\\u5357\\u65b9\\u5411\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 5\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u5b5d\\u611f\\u5317\\u65b9\\u5411\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 3\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6caa\\u84c9\\u7ebf\\u7ea2\\u5b89\\u897f\\u65b9\\u5411\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6caa\\u84c9\\u7ebf\\u6c49\\u53e3\\u65b9\\u5411\\u8fdb\\u573a\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u6b66\\u6c49\\u52a8\\u8f66\\u6240\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u54b8\\u5b81\\u5317\\u65b9\\u5411\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u6b66\\u4e5d\\u5ba2\\u4e13\\u845b\\u5e97\\u5357\\u65b9\\u5411\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u4eac\\u5e7f\\u9ad8\\u901f\\u5b5d\\u611f\\u5317\\u65b9\\u5411\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u6caa\\u84c9\\u7ebf\\u7ea2\\u5b89\\u897f\\u65b9\\u5411\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        },\n",
       "                        {\n",
       "                            \"source\": \"\\u6b66\\u6c49\\u7ad9\",\n",
       "                            \"target\": \"\\u6caa\\u84c9\\u7ebf\\u6c49\\u53e3\\u65b9\\u5411\\u79bb\\u573a\",\n",
       "                            \"value\": 0\n",
       "                        }\n",
       "                    ],\n",
       "                    \"left\": \"5%\",\n",
       "                    \"top\": \"5%\",\n",
       "                    \"right\": \"20%\",\n",
       "                    \"bottom\": \"5%\",\n",
       "                    \"nodeWidth\": 20,\n",
       "                    \"nodeGap\": 8,\n",
       "                    \"nodeAlign\": \"justify\",\n",
       "                    \"layoutIteration\": 32,\n",
       "                    \"orient\": \"horizontal\",\n",
       "                    \"draggable\": true,\n",
       "                    \"emphasis\": {\n",
       "                        \"focus\": \"none\"\n",
       "                    },\n",
       "                    \"label\": {\n",
       "                        \"show\": true,\n",
       "                        \"position\": \"bottom\",\n",
       "                        \"margin\": 8,\n",
       "                        \"fontSize\": 10\n",
       "                    },\n",
       "                    \"lineStyle\": {\n",
       "                        \"show\": true,\n",
       "                        \"width\": 1,\n",
       "                        \"opacity\": 2,\n",
       "                        \"curveness\": 0.5,\n",
       "                        \"type\": \"solid\"\n",
       "                    }\n",
       "                }\n",
       "            ],\n",
       "            \"title\": [\n",
       "                {\n",
       "                    \"show\": true,\n",
       "                    \"text\": \"Sankey-\\u57fa\\u672c\\u793a\\u4f8b\",\n",
       "                    \"target\": \"blank\",\n",
       "                    \"subtarget\": \"blank\",\n",
       "                    \"padding\": 5,\n",
       "                    \"itemGap\": 10,\n",
       "                    \"textAlign\": \"auto\",\n",
       "                    \"textVerticalAlign\": \"auto\",\n",
       "                    \"triggerEvent\": false\n",
       "                }\n",
       "            ],\n",
       "            \"tooltip\": {\n",
       "                \"show\": true,\n",
       "                \"trigger\": \"item\",\n",
       "                \"triggerOn\": \"mousemove|click\",\n",
       "                \"axisPointer\": {\n",
       "                    \"type\": \"line\"\n",
       "                },\n",
       "                \"showContent\": true,\n",
       "                \"alwaysShowContent\": false,\n",
       "                \"showDelay\": 0,\n",
       "                \"hideDelay\": 100,\n",
       "                \"enterable\": false,\n",
       "                \"confine\": false,\n",
       "                \"appendToBody\": false,\n",
       "                \"transitionDuration\": 0.4,\n",
       "                \"textStyle\": {\n",
       "                    \"fontSize\": 14\n",
       "                },\n",
       "                \"borderWidth\": 0,\n",
       "                \"padding\": 5,\n",
       "                \"order\": \"seriesAsc\"\n",
       "            },\n",
       "            \"color\": [\n",
       "                \"#5470c6\",\n",
       "                \"#91cc75\",\n",
       "                \"#fac858\",\n",
       "                \"#ee6666\",\n",
       "                \"#73c0de\",\n",
       "                \"#3ba272\",\n",
       "                \"#fc8452\",\n",
       "                \"#9a60b4\",\n",
       "                \"#ea7ccc\"\n",
       "            ]\n",
       "        }\n",
       "    ]\n",
       "};\n",
       "                chart_289d1c65aa1141c7be4911b5a27de93e.setOption(option_289d1c65aa1141c7be4911b5a27de93e);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x256a6f59610>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from pyecharts.charts import Line, Bar, Sankey, Timeline\n",
    "from pyecharts import options as opts\n",
    "import datetime\n",
    "\n",
    "maplist = ['武汉动车所',\n",
    "           '京广高速咸宁北方向', '武九客专葛店南方向',\n",
    "           '京广高速孝感北方向', '沪蓉线红安西方向',\n",
    "           '沪蓉线汉口方向', '丹水池联络线汉口方向']\n",
    "\n",
    "datafr = processTrains(\"wh1.csv\")\n",
    "\n",
    "\n",
    "def timedata(datafr: pandas.DataFrame, station: str, arrlea: str) -> list:\n",
    "    datafr[\"小时\"] = datafr[\"到时\"].apply(lambda x: x.hour)  # 分组时间列\n",
    "    # 按照车站和小时分组，小时用于保留数据便于后续操作\n",
    "    datafrgroup = datafr.groupby(by=[arrlea, \"小时\"]).count()\n",
    "    datafrgroup.reset_index(level=\"小时\", inplace=True)  # 解开时间索引便于后续获取数据\n",
    "\n",
    "    sttimedata = datafrgroup.loc[station]  # 按照车站获取数据\n",
    "\n",
    "    datares = [0]*24  # 承接返回值，24个0防止溢出\n",
    "\n",
    "    tl = sttimedata[\"小时\"].to_list()  # 时间序列，只使用第一个\n",
    "    cl = sttimedata[\"车次\"].to_list()  # 车次分布序列，用于画图映射\n",
    "\n",
    "    datares[tl[0]:len(tl)] = cl  # 覆盖数据为车次分布\n",
    "    return datares\n",
    "\n",
    "\n",
    "def timeplot(datafr: pandas.DataFrame, al: str, ti: str):\n",
    "    line = Line()  # 创建折线图实例\n",
    "\n",
    "    time_data = [datetime.datetime(2023, 11, 3, i) for i in range(0, 23)]\n",
    "    line.add_xaxis(time_data)  # 添加时间轴数据\n",
    "\n",
    "    for st in maplist:  # 添加 y 轴数据\n",
    "        line.add_yaxis(series_name=st, y_axis=timedata(datafr, st, al))\n",
    "    # 设置时间轴等全局设置\n",
    "    line.set_global_opts(xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(is_show=True),\n",
    "                                                  type_='time', axislabel_opts=opts.LabelOpts(is_show=True, rotate=-45,)),\n",
    "                         yaxis_opts=opts.AxisOpts(\n",
    "                             splitline_opts=opts.SplitLineOpts(is_show=True)),\n",
    "                         title_opts=opts.TitleOpts(\n",
    "                             title=ti, subtitle=\"数量时间变化图\"),\n",
    "                         legend_opts=opts.LegendOpts(pos_bottom='50%', pos_left=0, orient=\"vertical\"))\n",
    "\n",
    "    return line  # 渲染图表\n",
    "\n",
    "\n",
    "def sankeypolt(datafr: pandas.DataFrame):\n",
    "    tdarr = []\n",
    "    tdlea=[]\n",
    "    for st in maplist:\n",
    "        tdarr.append(timedata(datafr, st, \"arr\"))\n",
    "        tdlea.append(timedata(datafr, st, \"lea\"))\n",
    "\n",
    "    skdarr = pandas.DataFrame(data=tdarr, index=maplist)\n",
    "    skdataarr = skdarr.transpose().head(n=24)\n",
    "\n",
    "    skdlea = pandas.DataFrame(data=tdlea, index=maplist)\n",
    "    skddatalea = skdlea.transpose().head(n=24)\n",
    "   \n",
    "    tl = Timeline()\n",
    "    for i in range(0, 24):\n",
    "        rowlistarr = skdataarr.loc[i].to_list()\n",
    "\n",
    "        link1 = [{\"source\": skdataarr.columns[j]+\"进场\", \"target\": \"武汉站\",\n",
    "                  \"value\": int(rowlistarr[j])} for j in range(0, len(rowlistarr)-1)]\n",
    "        \n",
    "        rowlistlea = skddatalea.loc[i].to_list()\n",
    "        link2 = [{\"source\": \"武汉站\", \"target\":skddatalea.columns[j]+\"离场\" ,\n",
    "                  \"value\": int(rowlistlea[j])} for j in range(0, len(rowlistlea)-1)]\n",
    "        \n",
    "        node1=[{\"name\":skdataarr.columns[j]+\"进场\" } for j in range(0, len(rowlistarr)-1)]\n",
    "        node2=[{\"name\":skdataarr.columns[j]+\"离场\" } for j in range(0, len(rowlistarr)-1)]\n",
    "\n",
    "        node1.append({\"name\":\"武汉站\"})\n",
    "\n",
    "        sk = Sankey()\n",
    "        sk.add(\n",
    "            series_name=\"sankey\",\n",
    "            nodes=node1+node2,\n",
    "            links=link1+link2,\n",
    "            linestyle_opt=opts.LineStyleOpts(curve=0.5,opacity=2),\n",
    "            label_opts=opts.LabelOpts(position=\"bottom\",font_size=10),\n",
    "        )\n",
    "        sk.set_global_opts(title_opts=opts.TitleOpts(title=\"Sankey-基本示例\"))\n",
    "        sk.render_notebook()\n",
    "        tl.add(chart=sk, time_point=\"{0}时\".format(i))\n",
    "    return tl\n",
    "\n",
    "\n",
    "s = sankeypolt(datafr)\n",
    "s.render_notebook()\n",
    "#s.render(\"sank1.html\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/v5/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"c808fc0ebe8f4cf18c4304d80ffaede1\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_c808fc0ebe8f4cf18c4304d80ffaede1 = echarts.init(\n",
       "                    document.getElementById('c808fc0ebe8f4cf18c4304d80ffaede1'), 'white', {renderer: 'canvas'});\n",
       "                var option_c808fc0ebe8f4cf18c4304d80ffaede1 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"aria\": {\n",
       "        \"enabled\": false\n",
       "    },\n",
       "    \"color\": [\n",
       "        \"#5470c6\",\n",
       "        \"#91cc75\",\n",
       "        \"#fac858\",\n",
       "        \"#ee6666\",\n",
       "        \"#73c0de\",\n",
       "        \"#3ba272\",\n",
       "        \"#fc8452\",\n",
       "        \"#9a60b4\",\n",
       "        \"#ea7ccc\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"line\",\n",
       "            \"name\": \"\\u6b66\\u6c49\\u52a8\\u8f66\\u6240\",\n",
       "            \"connectNulls\": false,\n",
       "            \"xAxisIndex\": 0,\n",
       "            \"symbolSize\": 4,\n",
       "            \"showSymbol\": true,\n",
       "            \"smooth\": false,\n",
       "            \"clip\": true,\n",
       "            \"step\": false,\n",
       "            \"data\": [\n",
       "                [\n",
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       "                    2\n",
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       "                \"2023-11-03T22:00:00\"\n",
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       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
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       "                    \"type\": \"solid\"\n",
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       "    \"title\": [\n",
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       "            \"text\": \"\\u6b66\\u6c49\\u7ad9\\u5217\\u8f66\\u5230\\u8fbe\\u60c5\\u51b5\",\n",
       "            \"target\": \"blank\",\n",
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       "            \"textVerticalAlign\": \"auto\",\n",
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       "};\n",
       "                chart_c808fc0ebe8f4cf18c4304d80ffaede1.setOption(option_c808fc0ebe8f4cf18c4304d80ffaede1);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x20280b02950>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "timeplot(datafr, \"arr\", \"武汉站列车到达情况\").render_notebook()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
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     "execution_count": 35,
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